Timely information on the quality of feedstocks, process streams and products is crucial in many areas of the oil products and chemicals business, and the need to obtain more information from a given chemical sample has become more acute over the past decade. To this end, analytical chemistry has been transformed into a branch of chemistry with considerable overlap with other areas of science such as physics, mathematics, computer science and Artificial Intelligence science, so that the efficiency and speed of data extraction has increased many times. The term Chemometrics is used to refer to such computerized mathematical methods and interpretation of chemical data. Statistical analysis plays a central role in Chemometrics, and the main aim is to recognize a pattern in the chemical data generated from spectral analysis instruments.
Unfortunately, many physical test methods are still time consuming and are often performed solely in a quality control laboratory, with the result that the test procedures are not accessible for plant operations. To provide more timely results, Near-Infrared (NIR) analysis, using Chemometrics database construction techniques, was introduced in the industry to predict the physical properties and chemical composition of the products. The NIR technique offers enormous potential savings for refiners in monitoring, controlling and optimizing processes. A single NIR can substitute for many traditional analyzers and provide accurate and fast results on physical and chemical properties of process streams and refined products.
In order to use NIR, the chemical constituents and physical phenomena of interest must have direct or indirect absorbance in the NIR region. The time-consuming part of NIR work is the data analysis and modeling phase, where most of the work is done to find the correlation between NIR spectral characteristics and the property, or properties, of interest as measured by more traditional methods. The selected spectrum absorbencies on each sample and the correlated reference lab measurements are utilized as the databases in constructing the Chemometrics models (training-set samples). FIG. 1 is a schematic illustration of the creation of such a database.
So far, two separate NIR Chemometrics approaches have been developed for use in nearly every refinery for providing reliable quality control. These are known as PLSNIR (Partial Least Squares NIR) and TOPNIR (Topological NIR). These models allow prediction of the properties of unknown samples directly from their spectra. It must be emphasized that the reliability, accuracy and precision of properly calibrated and maintained NIR process analyzers are essential for the success of NIR as a closed-loop, feed-forward control system for on-stream performance.
Currently the TOPNIR and PLS modeling techniques, using measured NIR spectra of hydrocarbon streams, are capable of accurately predicting the following fuel, compositional and physical properties for comparison against ASTM approved methods:                RON (Research Octane Number)        MON (Motor Octane Number)        RVP (Reid Vapor Pressure)        Density        Distillation Cut Points (IBP, 5%, 10%, 20%, 30% . . . 95%, FBP)        Vapor over Liquid Ratio        Cloud Point        Flash Point        Freezing Point        Cetane Number        CFPP (Cold Filter Plugging Point)        PIONA (Paraffins, Iso-paraffins, Naphthenes, Aromatics)        % Benzene        % MTBE        % Tolune        % Xylenes        
These two Chemometrics modeling approaches each have their own methodology, advantages and disadvantages, and the relative accuracy of the TOPNIR and PLSNIR model predictions as a function of wavelength range and spectral resolution can be assessed using the same database.